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  Scaling of Information in Large Sensory Neuronal Populations

Cotton, R., Froudarakis, E., Ecker, A., Berens, P., Saggau, P., & Tolias, A. (2014). Scaling of Information in Large Sensory Neuronal Populations. Poster presented at AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles, Santorini, Greece.

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http://areadne.org/2014/home.html (Abstract)
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 Creators:
Cotton, RJ, Author
Froudarakis, E, Author
Ecker, AS1, 2, Author              
Berens, P, Author              
Saggau, P, Author
Tolias, AS, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Although we know a lot about how individual neurons in the brain represent the sensory environment, we are far from understanding how neural populations represent sensory information. Because individual neurons are noisy, pooling the activity of many neurons with similar response properties seems necessary to obtain an accurate representation of the sensory environment. However, it is widely believed that shared noise (or, noise correlations) in the activity of nearby neurons renders such pooling ineffective, profoundly limiting the accuracy of any population code and, ultimately, behavior. This belief is based on model-based extrapolations from correlations measured in individual pairs of neurons, as it has been impossible to record simultaneously from complete neuronal populations. Here, we use a novel 3D high-speed in vivo two-photon microscope to record nearly all of the hundreds of neurons in a small volume of the mouse primary visual cortex and directly measure the amount of information encoded by these local populations. In contrast to previous predictions, we find that the information in a sensory population increases approximately linearly with population size and does not saturate even for several hundred neurons. Moreover, even a decoder ignoring correlations between neurons can decode 80 of the information in the population. Our results suggest that sensory neural populations represent information in a truly distributed manner and pooling of neural activity within local circuits is much more effective than previously anticipated. Thus, the representation in early sensory areas does not appear to be impaired substantially by shared sensory noise and limitations in behavioral performance in psychophysical tasks may need to be attributed to processes downstream of the sensory population.

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 Dates: 2014-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: CottonFEBST2014
 Degree: -

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Title: AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles
Place of Event: Santorini, Greece
Start-/End Date: 2014-06-25 - 2014-06-29

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Title: AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles
Source Genre: Proceedings
 Creator(s):
Tatsopoulos, NG, Editor
Pezaris, JS, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : AREADNE Foundation
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 60 Identifier: ISSN: 2155-3203